Model-free learning of optimal beamformers for passive IRS-assisted sumrate maximization

TY - GEN

T1 - Model-free learning of optimal beamformers for passive IRS-assisted sumrate maximization

AU - Hashmi, Hassaan

AU - Pougkakiotis, Spyridon

AU - Kalogerias, Dionysis S.

N1 - Publisher Copyright: © 2023 IEEE.

PY - 2023/6/10

Y1 - 2023/6/10

N2 - Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing- free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does not require access to channel model or network structure information, and enables learning of optimal long-term IRS beamformers jointly with standard short-term precoding, based only on conventional effective channel state information. Supported by state-of-the-art (SOTA) convergence analysis, detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as well, consistently outperforming standard fully model-based baselines, in a variety of scenarios.

AB - Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing- free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does not require access to channel model or network structure information, and enables learning of optimal long-term IRS beamformers jointly with standard short-term precoding, based only on conventional effective channel state information. Supported by state-of-the-art (SOTA) convergence analysis, detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as well, consistently outperforming standard fully model-based baselines, in a variety of scenarios.

KW - Intelligent Reflecting Surfaces

KW - Model-Free Learning

KW - Sumrate Maximization

KW - Zeroth-order Optimization

UR - https://www.scopus.com/pages/publications/85177579762

U2 - 10.1109/ICASSP49357.2023.10095269

DO - 10.1109/ICASSP49357.2023.10095269

M3 - Conference contribution

SN - 9781728163284

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 1

EP - 5

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

PB - IEEE

T2 - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Y2 - 4 June 2023 through 10 June 2023

ER -